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  <div class="section" id="advanced-topics">
<span id="advanced"></span><h1>Advanced topics<a class="headerlink" href="#advanced-topics" title="Permalink to this headline">¶</a></h1>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">In this section we describe a number of the more advanced capabilities
of CVX. We recommend that you <em>skip</em> this section at first, until
you are comfortable with the basic capabilities described above.</p>
</div>
<div class="section" id="eliminating-quadratic-forms">
<span id="quad-forms"></span><h2>Eliminating quadratic forms<a class="headerlink" href="#eliminating-quadratic-forms" title="Permalink to this headline">¶</a></h2>
<p>One particular reformulation that we <em>strongly</em> encourage is to eliminate quadratic
forms&#8212;that is, functions like <tt class="docutils literal"><span class="pre">sum_square</span></tt>, <tt class="docutils literal"><span class="pre">sum(square(.))</span></tt> or <tt class="docutils literal"><span class="pre">quad_form</span></tt>&#8212;whenever
it is possible to construct equivalent models using <tt class="docutils literal"><span class="pre">norm</span></tt> instead.
Our experience tells us that quadratic forms often pose a numerical challenge for
the underlying solvers that CVX uses.</p>
<p>We acknowledge that this advice goes against conventional wisdom: quadratic forms
are the prototypical smooth convex function, while norms are nonsmooth and therefore
unwieldy. But with the <em>conic</em> solvers that CVX uses, this wisdom is <em>exactly backwards</em>.
It is the <em>norm</em> that is best suited for conic formulation and solution. Quadratic forms
are handled by <em>converting</em> them to a conic form&#8212;using norms, in fact! This conversion
process poses some interesting scaling challenges. It is better if the modeler can eliminate
the need to perform this conversion.</p>
<p>For a simple example of such a change, consider the objective</p>
<div class="highlight-none"><div class="highlight"><pre>minimize( sum_square( A * x - b ) )
</pre></div>
</div>
<p>In exact arithmetic, this is precisely equivalent to</p>
<div class="highlight-none"><div class="highlight"><pre>minimize( square_pos( norm( A * x - b ) ) )
</pre></div>
</div>
<p>But equivalence is also preserved if we eliminate the square altogether:</p>
<div class="highlight-none"><div class="highlight"><pre>minimize( norm( A * x - b ) )
</pre></div>
</div>
<p>The optimal value of <tt class="docutils literal"><span class="pre">x</span></tt> is identical in all three cases, but this last version is
likely to produce more accurate results. Of course, if you <em>need</em> the value of the
squared norm, you can always recover it by squaring the norm after the fact.</p>
<p>Conversions using <tt class="docutils literal"><span class="pre">quad_form</span></tt> can sometimes be a bit more difficult. For instance, consider</p>
<div class="highlight-none"><div class="highlight"><pre>quad_form( A * x - b, Q ) &lt;= 1
</pre></div>
</div>
<p>where <tt class="docutils literal"><span class="pre">Q</span></tt> is a positive definite matrix. The equivalent <tt class="docutils literal"><span class="pre">norm</span></tt> version is</p>
<div class="highlight-none"><div class="highlight"><pre>norm( Qsqrt * ( A * x - b ) ) &lt;= 1
</pre></div>
</div>
<p>where <tt class="docutils literal"><span class="pre">Qsqrt</span></tt> is an appropriate matrix square root of <tt class="docutils literal"><span class="pre">Q</span></tt>. One option is to compute
the symmetric square root <tt class="docutils literal"><span class="pre">Qsqrt</span> <span class="pre">=</span> <span class="pre">sqrtm(Q)</span></tt>, but this computation destroys sparsity.
If <tt class="docutils literal"><span class="pre">Q</span></tt> is sparse, it is likely worth the effort to compute a sparse Cholesky-based
square root:</p>
<div class="highlight-none"><div class="highlight"><pre>[ Qsqrt, p, S  ] = chol( Q );
Qsqrt = Qsqrt * S;
</pre></div>
</div>
<p>Sometimes an effective reformulation requires a practical understanding of what it
means for problems to be equivalent. For instance, suppose we wanted to add an
<span class="math">\(\ell_1\)</span> regularization term to the objective above, weighted by some fixed,
positive <tt class="docutils literal"><span class="pre">lambda</span></tt>:</p>
<div class="highlight-none"><div class="highlight"><pre>minimize( sum_square( A * x - b ) + lambda * norm( x, 1 ) )
</pre></div>
</div>
<p>In this case, we typically do not care about the <em>specific</em> values of <tt class="docutils literal"><span class="pre">lambda</span></tt>; rather
we are varying it over a range to study the tradeoff between the residual of <tt class="docutils literal"><span class="pre">A*x-b</span></tt>
and the 1-norm of <tt class="docutils literal"><span class="pre">x</span></tt>. The same tradeoff can be studied by examining this modified model:</p>
<div class="highlight-none"><div class="highlight"><pre>minimize( norm( A * x - b ) + lambda2 * norm( x, 1 ) )
</pre></div>
</div>
<p>This is not precisely the same model; setting <tt class="docutils literal"><span class="pre">lambda</span></tt> and <tt class="docutils literal"><span class="pre">lambda2</span></tt> to the same value
will not yield identical values of <tt class="docutils literal"><span class="pre">x</span></tt>. But both models <em>do</em> trace the same tradeoff
curve&#8212;only the second form is likely to produce more accurate results.</p>
</div>
<div class="section" id="indexed-dual-variables">
<span id="indexed-dual"></span><h2>Indexed dual variables<a class="headerlink" href="#indexed-dual-variables" title="Permalink to this headline">¶</a></h2>
<p>In some models, the <em>number</em> of constraints depends on the model
parameters&#8212;not just their sizes. It is straightforward to build such
models in CVX using, say, a Matlab <tt class="docutils literal"><span class="pre">for</span></tt> loop. In order to assign
each of these constraints a separate dual variable, we must find a way
to adjust the number of dual variables as well. For this reason, CVX
supports <em>indexed dual variables</em>. In reality, they are simply standard
Matlab cell arrays whose entries are CVX dual variable objects.</p>
<p>Let us illustrate by example how to declare and use indexed dual
variables. Consider the following semidefinite program from the
<a class="reference external" href="http://sedumi.ie.lehigh.edu">SeDuMi</a> examples:</p>
<div class="math">
\[\begin{split}\begin{array}{ll}
        \text{minimize} &amp; \sum_{i=1}^n (n-i) X_{ii} \\
        \text{subject to} &amp; \sum_{i=1}^n X_{i,i+k} = b_k, ~ k = 1,2,\dots,n \\
        &amp; X \succeq 0
\end{array}\end{split}\]</div>
<p>This problem minimizes a weighted sum of the main diagonal of a positive
semidefinite matrix, while holding the sums along each diagonal
constant. The parameters of the problem are the elements of the vector
<span class="math">\(b\in\mathbf{R}^n\)</span>, and the optimization variable is a symmetric
matrix <span class="math">\(X\in\mathbf{R}^{n\times n}\)</span>. The CVX version of this
model is</p>
<div class="highlight-none"><div class="highlight"><pre>cvx_begin
    variable X( n, n ) symmetric
    minimize( ( n - 1 : -1 : 0 ) * diag( X ) );
    for k = 0 : n-1,
        sum( diag( X, k ) ) == b( k+1 );
    end
    X == semidefinite(n);
cvx_end
</pre></div>
</div>
<p>If we wish to obtain dual information for the <span class="math">\(n\)</span> simple equality
constraints, we need a way to assign each constraint in the <tt class="docutils literal"><span class="pre">for</span></tt> loop
a separate dual variable. This is accomplished as follows:</p>
<div class="highlight-none"><div class="highlight"><pre>cvx_begin
    variable X( n, n ) symmetric
    dual variables y{n}
    minimize( ( n - 1 : -1 : 0 ) * diag( X ) );
    for k = 0 : n-1,
        sum( diag( X, k ) ) == b( k+1 ) : y{k+1};
    end
    X == semidefinite(n);
cvx_end
</pre></div>
</div>
<p>The statement <tt class="docutils literal"><span class="pre">dual</span> <span class="pre">variables</span> <span class="pre">y{n}</span></tt> allocates a cell array of
<span class="math">\(n\)</span> dual variables, and stores the result in the Matlab variable
<tt class="docutils literal"><span class="pre">Z</span></tt>. The equality constraint in the <tt class="docutils literal"><span class="pre">for</span></tt> loop has been augmented
with a reference to <tt class="docutils literal"><span class="pre">y{k+1}</span></tt>, so that each constraint is assigned a
separate dual variable. When the <tt class="docutils literal"><span class="pre">cvx_end</span></tt> command is issued, CVX
will compute the optimal values of these dual variables, and deposit
them into an <span class="math">\(n\)</span>-element cell array <tt class="docutils literal"><span class="pre">y</span></tt>.</p>
<p>This example admittedly is a bit simplistic. With a bit of careful
arrangement, it is possible to rewrite this model so that the <span class="math">\(n\)</span>
equality constraints can be combined into a single vector constraint,
which in turn would require only a single vector dual variable. <a class="footnote-reference" href="#id4" id="id1">[3]</a>
For a more complex example that is not amenable to such a
simplification, see the file</p>
<div class="highlight-none"><div class="highlight"><pre>examples/cvxbook/Ch07_statistical_estim/cheb.m
</pre></div>
</div>
<p>in the CVX distribution. In that problem, each constraint in the
<tt class="docutils literal"><span class="pre">for</span></tt> loop is a linear matrix inequality, not a scalar linear
equation; so the indexed dual variables are symmetric matrices, not
scalars.</p>
</div>
<div class="section" id="the-successive-approximation-method">
<span id="successive"></span><h2>The successive approximation method<a class="headerlink" href="#the-successive-approximation-method" title="Permalink to this headline">¶</a></h2>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">If you were referred to this web page by CVX&#8217;s warning message: welcome!
Please read this section carefully to fully understand why using
functions like <tt class="docutils literal"><span class="pre">log</span></tt>, <tt class="docutils literal"><span class="pre">exp</span></tt>, etc. within CVX models requires special care.</p>
</div>
<p>Prior to version 1.2, the functions <tt class="docutils literal"><span class="pre">exp</span></tt>, <tt class="docutils literal"><span class="pre">log</span></tt>, <tt class="docutils literal"><span class="pre">log_det</span></tt>,
and other functions from the exponential family could not be used within
CVX. Unfortunately, CVX utilizes symmetric primal/dual solvers that
simply cannot support those functions natively <a class="footnote-reference" href="#id5" id="id2">[4]</a>, and a variety of factors
prevent us from incorporating other types of solvers into CVX.</p>
<p>Nevertheless, support for these functions was requested quite frequently.
For this reason, we constructed a <em>successive approximation</em> heuristic that
allows the symmetric primal/dual solvers to support the exponential
family of functions. A precise description of the approach is beyond the
scope of this text, but roughly speaking, the method proceeds as follows:</p>
<ol class="arabic simple">
<li>Choose an initial approximation centerpoint <span class="math">\(x_c=0\)</span>.</li>
<li>Construct a polynomial approximation for each log/exp/entropy term
which is accurate in the neighborhood of <span class="math">\(x_c\)</span>.</li>
<li>Solve this approximate model to obtain its optimal point <span class="math">\(\bar{x}\)</span>.</li>
<li>If <span class="math">\(\bar{x}\)</span> satisfies the optimality conditions for
the <em>orignal</em> model to sufficient precision, exit.</li>
<li>Otherwise, shift <span class="math">\(x_c\)</span> towards <span class="math">\(\bar{x}\)</span>, and repeat steps 2-5.</li>
</ol>
<p>Again, this is a highly simplified description of the
approach; for instance, we actually employ both the primal and dual
solutions to guide our judgements for shifting <span class="math">\(x_c\)</span> and
terminating.</p>
<p>This approach has proven surprisingly effective for many problems.
<em>However, as with many heuristic approaches, it
is not perfect.</em> It will sometimes fail to converge even for problems known to have solutions.
Even when it does converge, it is several times slower than the standard solver,
due to its iterative approach. Therefore, it is best to use it sparingly and carefully.
Here are some specific usage tips:</p>
<ul>
<li><p class="first">First, confirm that the log/exp/entropy terms are truly necessary for your model. In
many cases, an exactly equivalent model can be constructed without them, and that should
always be preferred. For instance, the constraint</p>
<div class="highlight-none"><div class="highlight"><pre>sum_log(x) &gt;= 10
</pre></div>
</div>
<p>can be expressed in terms of the <cite>geo_mean</cite> function as</p>
<div class="highlight-none"><div class="highlight"><pre>geo_mean(x) &gt;= log(10)^(1/length(x))
</pre></div>
</div>
<p>Many determinant maximization problems are commonly written using <cite>log_det</cite>, but in
fact that is often unnecessary. For instance, consider the objective</p>
<div class="highlight-none"><div class="highlight"><pre>minimize( log_det(X) )
</pre></div>
</div>
<p>CVX actually converts this internally to this:</p>
<div class="highlight-none"><div class="highlight"><pre>minimize( n*log(det_rootn(X)) )
</pre></div>
</div>
<p>So what you can do instead is simply remove the logarithm, and solve this instead:</p>
<div class="highlight-none"><div class="highlight"><pre>minimize( det_rootn(X) )
</pre></div>
</div>
<p>The value of <tt class="docutils literal"><span class="pre">log_det(X)</span></tt> can simply be computed after the model is completed.
Unfortunately, this only
works if <tt class="docutils literal"><span class="pre">log_det</span></tt> is the only term in the objective; so, for instance, this
function cannot, unfortunately, be converted in a similar fashion:</p>
<div class="highlight-none"><div class="highlight"><pre>minimize( log_det(X) + trace(C*X) )
</pre></div>
</div>
</li>
<li><p class="first">Second, try different solvers. For instance, SeDuMi and MOSEK
tend to be more effective with the successive approximation method
than SDPT3. So if the default solver choice fails to give a
solution to your model, try switching to one of these solvers.</p>
</li>
<li><p class="first">Third, try smaller instances of your problem. If they succeed where
the larger instance fails, then at least you can confirm if the model
is behaving as you hope before considering alternative options like
a different solver.</p>
</li>
</ul>
<p>The bottom line, unfortunately, is that we cannot guarantee that
the successive approximation approach will successfully handle your
specific models. If you encounter problems, you are invited to submit
a bug report, but we will not be able to promise a fix.</p>
<div class="section" id="suppressing-the-warning">
<h3>Suppressing the warning<a class="headerlink" href="#suppressing-the-warning" title="Permalink to this headline">¶</a></h3>
<p>Because of all of these caveats, we believe that it is necessary to
issue a warning when it is used so that users understand its
experimental nature. This warning appears the first time you
attempt to specify a model in CVX that uses an function that
requires the successive approximation method. In fact, that warning
may very well have brought you to this section of the manual.</p>
<p>If you wish to suppress this warning in the future, simply issue
the command</p>
<div class="highlight-none"><div class="highlight"><pre>cvx_expert true
</pre></div>
</div>
<p>before you construct your model. If you wish to suppress this
message for all future sessions of MATLAB, follow this command
with the <tt class="docutils literal"><span class="pre">cvx_save_prefs</span></tt> command.</p>
</div>
</div>
<div class="section" id="power-functions-and-p-norms">
<span id="powerfunc"></span><h2>Power functions and p-norms<a class="headerlink" href="#power-functions-and-p-norms" title="Permalink to this headline">¶</a></h2>
<p>In order to implement the convex or concave branches of the power
function <span class="math">\(x^p\)</span> and <span class="math">\(p\)</span>-norms <span class="math">\(\|x\|_p\)</span> for general
values of <span class="math">\(p\)</span>, CVX uses an enhanced version of the SDP/SOCP
conversion method described by <a class="reference internal" href="credits.html#ag00" id="id3">[AG00]</a>.
This approach is exact&#8212;as long as the exponent <span class="math">\(p\)</span> is rational.
To determine integral values <span class="math">\(p_n,p_d\)</span> such that
<span class="math">\(p_n/p_d=p\)</span>, CVX uses Matlab&#8217;s <tt class="docutils literal"><span class="pre">rat</span></tt> function with its
default tolerance of <span class="math">\(10^{-6}\)</span>. There is currently no way to
change this tolerance. See the
<a class="reference external" href="http://www.mathworks.com/help/techdoc/ref/rat.html">MATLAB documentation</a>
for the <tt class="docutils literal"><span class="pre">rat</span></tt> function for more details.</p>
<p>The complexity of the resulting model depends roughly on the size of the
values <span class="math">\(p_n\)</span> and <span class="math">\(p_d\)</span>. Let us introduce a more precise
measure of this complexity. For <span class="math">\(p=2\)</span>, a constraint
<span class="math">\(x^p\leq y\)</span> can be represented with exactly one <span class="math">\(2\times 2\)</span>
LMI:</p>
<div class="math">
\[\begin{split}x^2 \leq y \quad\Longleftrightarrow\quad \begin{bmatrix} y &amp; x \\ x &amp; 1 \end{bmatrix} \succeq 0.\end{split}\]</div>
<p>For other values of <span class="math">\(p=p_n/p_d\)</span>, CVX generates a number of
<span class="math">\(2\times 2\)</span> LMIs that depends on both <span class="math">\(p_n\)</span> and <span class="math">\(p_d\)</span>;
we denote this number by <span class="math">\(k(p_n,p_d)\)</span>. (In some cases additional
linear constraints are also generated, but we ignore them for this
analysis.) For instance, for <span class="math">\(p=3/1\)</span>, we have</p>
<div class="math">
\[\begin{split}x^3\leq y, x\geq 0 \quad\Longleftrightarrow\quad \exists z ~
    \begin{bmatrix} z &amp; x \\ x &amp; 1 \end{bmatrix} \succeq 0. ~
    \begin{bmatrix} y &amp; z \\ z &amp; x \end{bmatrix} \succeq 0.\end{split}\]</div>
<p>So <span class="math">\(k(3,1)=2\)</span>. An empirical study has shown that for
<span class="math">\(p=p_n/p_d&gt;1\)</span>, we have</p>
<div class="math">
\[k(p_n,p_d)\leq\log_2 p_n+\alpha(p_n)\]</div>
<p>where the <span class="math">\(\alpha(p_n)\)</span> term grows very slowly compared to the
<span class="math">\(\log_2\)</span> term. Indeed, for <span class="math">\(p_n\leq 4096\)</span>, we have verified
that <span class="math">\(\alpha(p_n)\)</span> is usually 1 or 2, but occasionally 0 or 3.
Similar results are obtained for <span class="math">\(0 &lt; p &lt; 1\)</span> and <span class="math">\(p &lt; 0\)</span>.</p>
<p>The cost of this SDP representation is relatively small for nearly all
useful values of <span class="math">\(p\)</span>. Nevertheless, CVX issues a warning
whenever <span class="math">\(k(p_n,p_d)&gt;10\)</span> to insure that the user is not surprised
by any unexpected slowdown. In the event that this threshold does not
suit you, you may change it using the command
<tt class="samp docutils literal"><span class="pre">cvx_power_warning(</span><em><span class="pre">thresh</span></em><span class="pre">)</span></tt>, where <tt class="samp docutils literal"><em><span class="pre">thresh</span></em></tt> is the desired
cutoff value. Setting the threshold to <tt class="docutils literal"><span class="pre">Inf</span></tt> disables it completely.
As with the command <tt class="docutils literal"><span class="pre">cvx_precision</span></tt>, you can place a call to
<tt class="docutils literal"><span class="pre">cvx_power_warning</span></tt> within a model to change the threshold for a
single model; or outside of a model to make a global change. The command
always returns the <em>previous</em> value of the threshold, so you can save it
and restore it upon completion of your model, if you wish. You can query
the current value by calling <tt class="docutils literal"><span class="pre">cvx_power_warning</span></tt> with no arguments.</p>
</div>
<div class="section" id="overdetermined-problems">
<span id="overdetermined"></span><h2>Overdetermined problems<a class="headerlink" href="#overdetermined-problems" title="Permalink to this headline">¶</a></h2>
<p>The status message <tt class="docutils literal"><span class="pre">Overdetermined</span></tt> commonly occurs when structure
in a variable or set is not properly recognized. For example, consider
the problem of finding the smallest diagonal addition to a matrix
<span class="math">\(W\in\mathbf{R}^{n\times n}\)</span> to make it positive semidefinite:</p>
<div class="math">
\[\begin{split}\begin{array}{ll}
    \text{minimize}   &amp; \operatorname*{\textrm{Tr}}(D) \\
    \text{subject to} &amp; W + D \succeq 0 \\
                      &amp; D ~ \text{diagonal}
\end{array}\end{split}\]</div>
<p>In CVX, this problem might be expressed as follows:</p>
<div class="highlight-none"><div class="highlight"><pre>n = size(W,1);
cvx_begin
    variable D(n,n) diagonal;
    minimize( trace( D ) );
    subject to
        W + D == semidefinite(n);
cvx_end
</pre></div>
</div>
<p>If we apply this specification to the matrix <tt class="docutils literal"><span class="pre">W=randn(5,5)</span></tt>, a warning
is issued,</p>
<div class="highlight-none"><div class="highlight"><pre>Warning: Overdetermined equality constraints;
    problem is likely infeasible.
</pre></div>
</div>
<p>and the variable <tt class="docutils literal"><span class="pre">cvx_status</span></tt> is set to <tt class="docutils literal"><span class="pre">Overdetermined</span></tt>.</p>
<p>What has happened here is that the unnamed variable returned by
statement <tt class="docutils literal"><span class="pre">semidefinite(n)</span></tt> is <em>symmetric</em>, but <span class="math">\(W\)</span> is fixed and
<em>unsymmetric</em>. Thus the problem, as stated, is infeasible. But there are
also <span class="math">\(n^2\)</span> equality constraints here, and only <span class="math">\(n+n*(n+1)/2\)</span>
unique degrees of freedom&#8212;thus the problem is overdetermined. We can
correct this problem by replacing the equality constraint with</p>
<div class="highlight-none"><div class="highlight"><pre>sym( W ) + D == semidefinite(n);
</pre></div>
</div>
<p><tt class="docutils literal"><span class="pre">sym</span></tt> is a function we have provided that extracts the symmetric part
of its argument; that is, <tt class="docutils literal"><span class="pre">sym(W)</span></tt> equals <tt class="docutils literal"><span class="pre">0.5</span> <span class="pre">*</span> <span class="pre">(</span> <span class="pre">W</span> <span class="pre">+</span> <span class="pre">W'</span> <span class="pre">)</span></tt>.</p>
</div>
<div class="section" id="adding-new-functions-to-the-atom-library">
<span id="newfunc"></span><h2>Adding new functions to the atom library<a class="headerlink" href="#adding-new-functions-to-the-atom-library" title="Permalink to this headline">¶</a></h2>
<p>CVX allows new convex and concave functions to be defined and added
to the atom library, in two ways, described in this section. The first
method is simple, and can (and should) be used by many users of CVX,
since it requires only a knowledge of the basic DCP ruleset. The second
method is very powerful, but a bit complicated, and should be considered
an advanced technique, to be attempted only by those who are truly
comfortable with convex analysis, disciplined convex programming, and
CVX in its current state.</p>
<p>Please let us know if you have implemented a convex or concave
function that you think would be useful to other users; we will be happy
to incorporate it in a future release.</p>
<div class="section" id="new-functions-via-the-dcp-ruleset">
<h3>New functions via the DCP ruleset<a class="headerlink" href="#new-functions-via-the-dcp-ruleset" title="Permalink to this headline">¶</a></h3>
<p>The simplest way to construct a new function that works within CVX
is to construct it using expressions that fully conform to the DCP
ruleset. Consider, for instance, the deadzone function</p>
<div class="math">
\[\begin{split}f(x) = \max \{ |x|-1, 0 \} = \begin{cases} 0 &amp; |x| \leq 1\\ x-1 &amp; x &gt; 1 \end{cases}\end{split}\]</div>
<p>To implement this function in CVX, simply create a file
<tt class="docutils literal"><span class="pre">deadzone.m</span></tt> containing</p>
<div class="highlight-none"><div class="highlight"><pre>function y = deadzone( x )
y = max( abs( x ) - 1, 0 )
</pre></div>
</div>
<p>This function works just as you expect it would outside of
CVX &#8212; that is, when its argument is numerical. But thanks to Matlab&#8217;s
operator overloading capability, it will also work within CVX if
called with an affine argument. CVX will properly conclude that the
function is convex, because all of the operations carried out conform to
the rules of DCP: <tt class="docutils literal"><span class="pre">abs</span></tt> is recognized as a convex function; we can
subtract a constant from it, and we can take the maximum of the result
and <tt class="docutils literal"><span class="pre">0</span></tt>, which yields a convex function. So we are free to use
<tt class="docutils literal"><span class="pre">deadzone</span></tt> anywhere in a CVX specification that we might use
<tt class="docutils literal"><span class="pre">abs</span></tt>, for example, because CVX knows that it is a convex
function.</p>
<p>Let us emphasize that when defining a function this way, the expressions
you use <em>must</em> conform to the DCP ruleset, just as they would if they
had been inserted directly into a CVX model. For example, if we
replace <tt class="docutils literal"><span class="pre">max</span></tt> with <tt class="docutils literal"><span class="pre">min</span></tt> above; <em>e.g.</em>,</p>
<div class="highlight-none"><div class="highlight"><pre>function y = deadzone_bad( x )
y = min( abs( x ) - 1, 0 )
</pre></div>
</div>
<p>then the modified function fails to satisfy the DCP ruleset. The function
will work <em>outside</em> of a CVX specification, happily computing the
value <span class="math">\(\min \{|x|-1,0\}\)</span> for a <em>numerical</em> argument <span class="math">\(x\)</span>. But
inside a CVX specification, invoked with a nonconstant argument, it
will generate an error.</p>
</div>
<div class="section" id="new-functions-via-partially-specified-problems">
<span id="newfunc-psp"></span><h3>New functions via partially specified problems<a class="headerlink" href="#new-functions-via-partially-specified-problems" title="Permalink to this headline">¶</a></h3>
<p>A more advanced method for defining new functions in CVX relies on
the following basic result of convex analysis. Suppose that
<span class="math">\(S\subset\mathbf{R}^n\times\mathbf{R}^m\)</span> is a convex set and
<span class="math">\(g:(\mathbf{R}^n\times\mathbf{R}^m)\rightarrow(\mathbf{R}\cup+\infty)\)</span>
is a convex function. Then</p>
<div class="math">
\[f:\mathbf{R}^n\rightarrow(\mathbf{R}\cup+\infty), \quad f(x) \triangleq \inf\left\{\,g(x,y)\,~|~\,\exists y,~(x,y)\in S \,\right\}\]</div>
<p>is also a convex function. (This rule is sometimes called the <em>partial
minimization rule</em>.) We can think of the convex function <span class="math">\(f\)</span> as
the optimal value of a family of convex optimization problems, indexed
or parametrized by <span class="math">\(x\)</span>,</p>
<div class="math">
\[\begin{split}\begin{array}{ll}
    \mbox{minimize} &amp; g(x,y) \\
    \mbox{subject to} &amp; (x,y) \in S
\end{array}\end{split}\]</div>
<p>with optimization variable <span class="math">\(y\)</span>.</p>
<p>One special case should be very familiar: if <span class="math">\(m=1\)</span> and
<span class="math">\(g(x,y)\triangleq y\)</span>, then</p>
<div class="math">
\[f(x) \triangleq \inf\left\{\,y\,~|~\,\exists y,~(x,y)\in S\,\right\}\]</div>
<p>gives the classic <em>epigraph</em> representation of <span class="math">\(f\)</span>:</p>
<div class="math">
\[\operatorname{\textbf{epi}}f = S+ \left( \{ 0 \} \times \mathbf{R}_+ \right),\]</div>
<p>where <span class="math">\(0 \in \mathbf{R}^n\)</span>.</p>
<p>In CVX you can define a convex function in this very manner, that
is, as the optimal value of a parameterized family of disciplined convex
programs. We call the underlying convex program in such cases an
<em>incomplete specification</em>&#8212;so named because the parameters (that is,
the function inputs) are unknown when the specification is constructed.
The concept of incomplete specifications can at first seem a bit
complicated, but it is very powerful mechanism that allows CVX to
support a wide variety of functions.</p>
<p>Let us look at an example to see how this works. Consider the
unit-halfwidth Huber penalty function <span class="math">\(h(x)\)</span>:</p>
<div class="math">
\[\begin{split}h:\mathbf{R}\rightarrow\mathbf{R}, \quad h(x) \triangleq \begin{cases} x^2 &amp; |x| \leq 1 \\ 2|x|-1 &amp; |x| \geq 1 \end{cases}.\end{split}\]</div>
<p>We can express the Huber function in terms of the following family of
convex QPs, parameterized by <span class="math">\(x\)</span>:</p>
<div class="math">
\[\begin{split}\begin{array}{ll}
    \text{minimize}   &amp; 2 v + w^2 \\
    \text{subject to} &amp; | x | \leq v + w \\
                      &amp; w \leq 1, ~ v \geq 0
\end{array}\end{split}\]</div>
<p>with scalar variables <span class="math">\(v\)</span> and <span class="math">\(w\)</span>. The optimal value of this
simple QP is equal to the Huber penalty function of <span class="math">\(x\)</span>. We note
that the objective and constraint functions in this QP are (jointly)
convex in <span class="math">\(v\)</span>, <span class="math">\(w\)</span> <em>and</em> <span class="math">\(x\)</span>.</p>
<p>We can implement the Huber penalty function in CVX as follows:</p>
<div class="highlight-none"><div class="highlight"><pre>function cvx_optval = huber( x )
cvx_begin
    variables w v;
    minimize( w^2 + 2 * v );
    subject to
        abs( x ) &lt;= w + v;
        w &lt;= 1; v &gt;= 0;
cvx_end
</pre></div>
</div>
<p>If <tt class="docutils literal"><span class="pre">huber</span></tt> is called with a numeric value of <tt class="docutils literal"><span class="pre">x</span></tt>, then upon reaching
the <tt class="docutils literal"><span class="pre">cvx_end</span></tt> statement, CVX will find a complete specification,
and solve the problem to compute the result. CVX places the optimal
objective function value into the variable <tt class="docutils literal"><span class="pre">cvx_optval</span></tt>, and function
returns that value as its output. Of course, it&#8217;s very inefficient to
compute the Huber function of a numeric value <span class="math">\(x\)</span> by solving a QP.
But it does give the correct value (up to the core solver accuracy).</p>
<p>What is most important, however, is that if <tt class="docutils literal"><span class="pre">huber</span></tt> is used within a
CVX specification, with an affine CVX expression for its
argument, then CVX will do the right thing. In particular, CVX
will recognize the Huber function, called with affine argument, as a
valid convex expression. In this case, the function <tt class="docutils literal"><span class="pre">huber</span></tt> will
contain a special Matlab object that represents the function call in
constraints and objectives. Thus the function <tt class="docutils literal"><span class="pre">huber</span></tt> can be used
anywhere a traditional convex function can be used, in constraints or
objective functions, in accordance with the DCP ruleset.</p>
<p>There is a corresponding development for concave functions as well.
Given a convex set <span class="math">\(S\)</span> as above, and a concave function
<span class="math">\(g:(\mathbf{R}^n\times\mathbf{R}^m)\rightarrow(\mathbf{R}\cup-\infty)\)</span>,
the function</p>
<div class="math">
\[f:\mathbf{R}\rightarrow(\mathbf{R}\cup-\infty), \quad f(x) \triangleq \sup\left\{\,g(x,y)\,~|~\,\exists y,~(x,y)\in S \,\right\}\]</div>
<p>is concave. If <span class="math">\(g(x,y)\triangleq y\)</span>, then</p>
<div class="math">
\[f(x) \triangleq \sup\left\{\,y\,~|~\,\exists y,~(x,y)\in S\,\right\}\]</div>
<p>gives the <em>hypograph</em> representation of <span class="math">\(f\)</span>:</p>
<div class="math">
\[\operatorname{\textbf{hypo}}f = S - \mathbf{R}_+^n.\]</div>
<p>In CVX, a concave incomplete specification is simply one that uses a
<tt class="docutils literal"><span class="pre">maximize</span></tt> objective instead of a <tt class="docutils literal"><span class="pre">minimize</span></tt> objective; and if
properly constructed, it can be used anywhere a traditional concave
function can be used within a CVX specification.</p>
<p>For an example of a concave incomplete specification, consider the
function</p>
<div class="math">
\[f:\mathbf{R}^{n\times n}\rightarrow\mathbf{R}, \quad f(X) = \lambda_{\min}(X+X^T)\]</div>
<p>Its hypograph can be represented using a single linear matrix
inequality:</p>
<div class="math">
\[\operatorname{\textbf{hypo}}f = \left\{\, (X,t) \,~|~\, f(X) \geq t \,\right\} = \left\{\, (X,t) \,~|~\, X + X^T - t I \succeq 0 \,\right\}\]</div>
<p>So we can implement this function in CVX as follows:</p>
<div class="highlight-none"><div class="highlight"><pre>function cvx_optval = lambda_min_symm( X )
n = size( X, 1 );
cvx_begin
    variable y;
    maximize( y );
    subject to
        X + X&#39; - y * eye( n ) == semidefinite( n );
cvx_end
</pre></div>
</div>
<p>If a numeric value of <tt class="docutils literal"><span class="pre">X</span></tt> is supplied, this function will return
<tt class="docutils literal"><span class="pre">min(eig(X+X'))</span></tt> (to within numerical tolerances). However, this
function can also be used in CVX constraints and objectives, just
like any other concave function in the atom library.</p>
<p>There are two practical issues that arise when defining functions using
incomplete specifications, both of which we will illustrate using our
<tt class="docutils literal"><span class="pre">huber</span></tt> example above. First of all, as written the function works
only with scalar values. To apply it (elementwise) to a vector requires
that we iterate through the elements in a <tt class="docutils literal"><span class="pre">for</span></tt> loop&#8212;a <em>very</em>
inefficient enterprise, particularly in CVX. A far better approach
is to extend the <tt class="docutils literal"><span class="pre">huber</span></tt> function to handle vector inputs. This is, in
fact, rather simple to do: we simply create a <em>multiobjective</em> version
of the problem:</p>
<div class="highlight-none"><div class="highlight"><pre>function cvx_optval = huber( x )
sx = size( x );
cvx_begin
    variables w( sx ) v( sx );
    minimize( w .^ 2 + 2 * v );
    subject to
        abs( x ) &lt;= w + v;
        w &lt;= 1; v &gt;= 0;
cvx_end
</pre></div>
</div>
<p>This version of <tt class="docutils literal"><span class="pre">huber</span></tt> will in effect create <tt class="docutils literal"><span class="pre">sx</span></tt> &#8220;instances&#8221; of
the problem in parallel; and when used in a CVX specification, will
be handled correctly.</p>
<p>The second issue is that if the input to <tt class="docutils literal"><span class="pre">huber</span></tt> is numeric, then
direct computation is a far more efficient way to compute the result
than solving a QP. (What is more, the multiobjective version cannot be
used with numeric inputs.) One solution is to place both versions in one
file, with an appropriate test to select the proper version to use:</p>
<div class="highlight-none"><div class="highlight"><pre>function cvx_optval = huber( x )
if isnumeric( x ),
    xa   = abs( x );
    flag = xa &lt; 1;
    cvx_optval = flag .* xa.^2 + (~flag) * (2*xa-1);
else,
    sx = size( x );
    cvx_begin
        variables w( sx ) v( sx );
        minimize( w .^ 2 + 2 * v );
        subject to
            abs( x ) &lt;= w + v;
            w &lt;= 1; v &gt;= 0;
    cvx_end
end
</pre></div>
</div>
<p>Alternatively, you can create two separate versions of the function, one
for numeric input and one for CVX expressions, and place the CVX
version in a subdirectory called <tt class="docutils literal"><span class="pre">&#64;cvx</span></tt>. (Do not include this
directory in your Matlab <tt class="docutils literal"><span class="pre">path</span></tt>; only include its parent.) Matlab will
automatically call the version in the <tt class="docutils literal"><span class="pre">&#64;cvx</span></tt> directory when one of the
arguments is a CVX variable. This is the approach taken for the
version of <tt class="docutils literal"><span class="pre">huber</span></tt> found in the CVX atom library.</p>
<p>One good way to learn more about using incomplete specifications is to
examine some of the examples already in the CVX atom library. Good
choices include <tt class="docutils literal"><span class="pre">huber</span></tt>, <tt class="docutils literal"><span class="pre">inv_pos</span></tt>, <tt class="docutils literal"><span class="pre">lambda_min</span></tt>, <tt class="docutils literal"><span class="pre">lambda_max</span></tt>,
<tt class="docutils literal"><span class="pre">matrix_frac</span></tt>, <tt class="docutils literal"><span class="pre">quad_over_lin</span></tt>, <tt class="docutils literal"><span class="pre">sum_largest</span></tt>, and others. Some
are a bit difficult to read because of diagnostic or error-checking
code, but these are relatively simple.</p>
<table class="docutils footnote" frame="void" id="id4" rules="none">
<colgroup><col class="label" /><col /></colgroup>
<tbody valign="top">
<tr><td class="label"><a class="fn-backref" href="#id1">[3]</a></td><td>Indeed, a future version of CVX will support the use of the
Matlab function <tt class="docutils literal"><span class="pre">spdiags</span></tt>, which will reduce the entire for loop to
the single constraint <tt class="docutils literal"><span class="pre">spdiags(X,0:n-1)==b</span></tt>.</td></tr>
</tbody>
</table>
<table class="docutils footnote" frame="void" id="id5" rules="none">
<colgroup><col class="label" /><col /></colgroup>
<tbody valign="top">
<tr><td class="label"><a class="fn-backref" href="#id2">[4]</a></td><td>Technically there are a couple of exceptions here. First of all,
SDPT3 does, in fact, support the existence of logarithms and <tt class="docutils literal"><span class="pre">log_det</span></tt>
terms in the objective function. However, it doesn&#8217;t support such terms
within constraints. Unfortunately, because CVX does not differentiate
between objective terms and constraint terms internally, it is not able
to utilize this capability of SDPT3. Secondly, this section was written
before the inclusion of MOSEK support in CVX, and CVX does indeed provide
support for smooth nonlinearities in its solver. But this capability
is not easy to use in MATLAB.</td></tr>
</tbody>
</table>
</div>
</div>
</div>


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    <ul>
<li><a class="reference internal" href="#">Advanced topics</a><ul>
<li><a class="reference internal" href="#eliminating-quadratic-forms">Eliminating quadratic forms</a></li>
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<li><a class="reference internal" href="#new-functions-via-the-dcp-ruleset">New functions via the DCP ruleset</a></li>
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